code: def _predict_spark_xshards(self, xshards, param):
      ray_xshards = RayXShards.from_spark_xshards(xshards)
  
      def transform_func(worker, shards_ref):
          data_creator = lambda config, batch_size: shards_ref
          return worker.predict.remote(data_creator, **param)
  
      pred_shards = ray_xshards.transform_shards_with_actors(
          self.remote_workers, transform_func
      )
      spark_xshards = pred_shards.to_spark_xshards()
      return spark_xshards
  
imports: import io
  import types
  import logging
  import numbers
  import torch
  import numpy
  from zoo.orca.data.ray_xshards import RayXShards
  from zoo.orca.learn.pytorch.training_operator import TrainingOperator
  from zoo.orca.learn.pytorch.torch_runner import TorchRunner
  from zoo.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, update_predict_xshards, process_xshards_of_pandas_dataframe
  from zoo.ray import RayContext
  import ray
  from ray.exceptions import RayActorError
label_desc: Use image node_id 6 for calling the PyTorchRayEstimator obj's underlying member method code with example usage: obj._predict_spark_xshards(xshards, param) and returns: spark_xshards, worker